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Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Kizilcec, René F. · Piech, Chris · Schneider, Emily

PublishedApril 2013
ConferenceLAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge
Pages 170-179
CountryUnited States, North America

ABSTRACT
As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we present a simple, scalable, and informative classification method that identifies a small number of longitudinal engagement trajectories in MOOCs. Learners are classified based on their patterns of interaction with video lectures and assessments, the primary features of most MOOCs to date.

In an analysis of three computer science MOOCs, the classifier consistently identifies four prototypical trajectories of engagement. The most notable of these is the learners who stay engaged through the course without taking assessments. These trajectories are also a useful framework for the comparison of learner engagement between different course structures or instructional approaches. We compare learners in each trajectory and course across demographics, forum participation, video access, and reports of overall experience. These results inform a discussion of future interventions, research, and design directions for MOOCs. Potential improvements to the classification mechanism are also discussed, including the introduction of more fine-grained analytics.

Keywords MOOC · learner engagement · trajectories of engagement

Published atCalifornia
LanguageEnglish
RefereedYes
DOI10.1145/2460296.2460330
Export optionsBibTex · EndNote · Tagged XML · Google Scholar


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